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A Computational Study on different penalty approaches for constrained optimization in radiation therapy treatment planning with a simulated annealing algorithm
Title
A Computational Study on different penalty approaches for constrained optimization in radiation therapy treatment planning with a simulated annealing algorithm.
Creator
Mohammadi Khoroushadi, Mohammad Sadegh, Shang, Charles, Ouhib, Zoubir, Graduate College, Leventouri, Theodora, Kalantzis, Georgios
Abstract/Description
Simulated Annealing algorithm is utilized for Intensity Modulated Radiation Therapy IMRT optimization. The goal in IMRT is to give the prescribed radiation dose to the tumor while minimizing the dose given to normal organs.
Date Issued
2015
PURL
http://purl.flvc.org/fau/fd/FA00005891
Format
Document (PDF)
Dosimetric and radiobiological comparison of CyberKnife M6(TM) InCise multileaf collimator over IRIS(TM) variable collimator in prostate stereotactic body radiation therapy
Title
Dosimetric and radiobiological comparison of CyberKnife M6(TM) InCise multileaf collimator over IRIS(TM) variable collimator in prostate stereotactic body radiation therapy.
Creator
Kathriarachchi, Vindu, Shang, Charles, Evans, Grant, Leventouri, Theodora, Kalantzis, Georgios
Date Issued
2016
PURL
http://purl.flvc.org/fau/fd/FAUIR000166
Format
Citation
Investigation of Mathematical Modeling for the general treatment of Glioblastoma
Title
Investigation of Mathematical Modeling for the general treatment of Glioblastoma.
Creator
Khatiwada, Dharma Raj, Kalantzis, Georgios, Florida Atlantic University, Charles E. Schmidt College of Science, Department of Physics
Abstract/Description
The purpose of this research is to validate various forms of mathematical modeling of glioblastoma multiforme (GBM) expressed as differential equations, numerically. The first work was involved in the numerical solution of the reaction-convection model, efficacy of which is expressed in terms of survival time. It was calculated using simple numerical scheme for the standard-of-care treatment in clinics which includes surgery followed by the radiation and chemotherapy. Survival time using all...
Show moreThe purpose of this research is to validate various forms of mathematical modeling of glioblastoma multiforme (GBM) expressed as differential equations, numerically. The first work was involved in the numerical solution of the reaction-convection model, efficacy of which is expressed in terms of survival time. It was calculated using simple numerical scheme for the standard-of-care treatment in clinics which includes surgery followed by the radiation and chemotherapy. Survival time using all treatment options increased significantly to 57 weeks compared to that of surgery close to 14 weeks. It was also observed that survival time increased significantly to 90 weeks if tumor is totally resected. In reaction-diffusion model using simple numerical scheme, tumor cell density patterns due to variation in patient specific tumor parameters such as net proliferation rate and diffusion coefficient were computed. Significant differences were observed in the patterns while using dominant diffusion and proliferation rate separately. Numerical solution of the tumor growth model under the anti-angiogenic therapy revealed some impacts in optimum tumor growth control however it was not significant.
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Date Issued
2016
PURL
http://purl.flvc.org/fau/fd/FA00004703
Subject Headings
Antineoplastic agents, Brain -- Cancer -- Treatment, Cancer -- Research, Cytology, Glioblastoma multiforme -- Treatment, Immune system -- Mathematical models, Systems biology
Format
Document (PDF)
Manufacturing of 3D Printed Boluses for Use In Electron Radiation Therapy
Title
Manufacturing of 3D Printed Boluses for Use In Electron Radiation Therapy.
Creator
Gibbard, Grant, Kalantzis, Georgios, Florida Atlantic University, Charles E. Schmidt College of Science, Department of Physics
Abstract/Description
This research demonstrates that a 3D printed bolus can be customized for electron radiation therapy. Both extruder and powder based printers were used, along with, paraffin wax, super stuff, and H20. The plan dose coverage and conformity for the planning target volume (PTV), was such that the distal side of the PTV was covered by the 90% isodose line. The structure is read, and converted into an STL file. The file is sent to a slicer to print. The object was filled with parafin wax,...
Show moreThis research demonstrates that a 3D printed bolus can be customized for electron radiation therapy. Both extruder and powder based printers were used, along with, paraffin wax, super stuff, and H20. The plan dose coverage and conformity for the planning target volume (PTV), was such that the distal side of the PTV was covered by the 90% isodose line. The structure is read, and converted into an STL file. The file is sent to a slicer to print. The object was filled with parafin wax, superstuff or water and sealed. Materials Hounsfield units were analyzed, along with the structure stability. This method is evaluated by scanning the 3D printed bolus. The dose conformity is improved compared to that with no bolus. By generating a patient specific 3D printed bolus there is an in improvement in conformity of the prescription isodose surface while sparing immediately adjacent normal tissues.
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Date Issued
2017
PURL
http://purl.flvc.org/fau/fd/FA00005943
Subject Headings
Dissertations, Academic -- Florida Atlantic University, Radiotherapy Dosage., Skin--Cancer., Radiotherapy--methods
Format
Document (PDF)
Sparse Modeling Applied to Patient Identification for Safety in Medical Physics Applications
Title
Sparse Modeling Applied to Patient Identification for Safety in Medical Physics Applications.
Creator
Lewkowitz, Stephanie, Kalantzis, Georgios, Florida Atlantic University, Charles E. Schmidt College of Science, Department of Physics
Abstract/Description
Every scheduled treatment at a radiation therapy clinic involves a series of safety protocol to ensure the utmost patient care. Despite safety protocol, on a rare occasion an entirely preventable medical event, an accident, may occur. Delivering a treatment plan to the wrong patient is preventable, yet still is a clinically documented error. This research describes a computational method to identify patients with a novel machine learning technique to combat misadministration.The patient...
Show moreEvery scheduled treatment at a radiation therapy clinic involves a series of safety protocol to ensure the utmost patient care. Despite safety protocol, on a rare occasion an entirely preventable medical event, an accident, may occur. Delivering a treatment plan to the wrong patient is preventable, yet still is a clinically documented error. This research describes a computational method to identify patients with a novel machine learning technique to combat misadministration.The patient identification program stores face and fingerprint data for each patient. New, unlabeled data from those patients are categorized according to the library. The categorization of data by this face-fingerprint detector is accomplished with new machine learning algorithms based on Sparse Modeling that have already begun transforming the foundation of Computer Vision. Previous patient recognition software required special subroutines for faces and diā†µerent tailored subroutines for fingerprints. In this research, the same exact model is used for both fingerprints and faces, without any additional subroutines and even without adjusting the two hyperparameters. Sparse modeling is a powerful tool, already shown utility in the areas of super-resolution, denoising, inpainting, demosaicing, and sub-nyquist sampling, i.e. compressed sensing. Sparse Modeling is possible because natural images are inherrently sparse in some bases, due to their inherrant structure. This research chooses datasets of face and fingerprint images to test the patient identification model. The model stores the images of each dataset as a basis (library). One image at a time is removed from the library, and is classified by a sparse code in terms of the remaining library. The Locally Competetive Algorithm, a truly neural inspired Artificial Neural Network, solves the computationally difficult task of finding the sparse code for the test image. The components of the sparse representation vector are summed by `1 pooling, and correct patient identification is consistently achieved 100% over 1000 trials, when either the face data or fingerprint data are implemented as a classification basis. The algorithm gets 100% classification when faces and fingerprints are concatenated into multimodal datasets. This suggests that 100% patient identification will be achievable in the clinal setting.
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Date Issued
2016
PURL
http://purl.flvc.org/fau/fd/FA00004721, http://purl.flvc.org/fau/fd/FA00004721
Subject Headings
Computer vision in medicine, Diagnostic imaging -- Data processing, Mathematical models, Medical errors -- Prevention, Medical physics, Sampling (Statistics)
Format
Document (PDF)
Dosimetric Consequences of the Parotid Glands Using CT-To-CBCT Deformable Registration During IMRT For Late Stage Head And Neck Cancers
Title
Dosimetric Consequences of the Parotid Glands Using CT-To-CBCT Deformable Registration During IMRT For Late Stage Head And Neck Cancers.
Creator
Conill, Annette L., Selvaraj, Raj, Kalantzis, Georgios, Florida Atlantic University, Charles E. Schmidt College of Science, Department of Physics
Abstract/Description
Patients receiving Intensity Modulated Radiation Therapy (IMRT) for late stage head and neck (HN) cancer often experience anatomical changes due to weight loss, tumor regression, and positional changes of normal anatomy (1). As a result, the actual dose delivered may vary from the original treatment plan. The purpose of this study was (a) to evaluate the dosimetric consequences of the parotid glands during the course of treatment, and (b) to determine if there would be an optimal timeframe...
Show morePatients receiving Intensity Modulated Radiation Therapy (IMRT) for late stage head and neck (HN) cancer often experience anatomical changes due to weight loss, tumor regression, and positional changes of normal anatomy (1). As a result, the actual dose delivered may vary from the original treatment plan. The purpose of this study was (a) to evaluate the dosimetric consequences of the parotid glands during the course of treatment, and (b) to determine if there would be an optimal timeframe for replanning. Nineteen locally advanced HN cancer patients underwent definitive IMRT. Each patient received an initial computerized tomography simulation (CT-SIM) scan and weekly cone beam computerized tomography (CBCT) scans. A Deformable Image Registration (DIR) was performed between the CT-SIM and CBCT of the parotid glands and Planning Target Volumes (PTVs) using the Eclipse treatment planning system (TPS) and the Velocity deformation software. A recalculation of the dose was performed on the weekly CBCTs using the original monitor units. The parameters for evaluation of our method were: the changes in volume of the PTVs and parotid glands, the dose coverage of the PTVs, the lateral displacement in the Center of Mass (COM), the mean dose, and Normal Tissue Complication Probability (NTCP) of the parotid glands. The studies showed a reduction of the volume in the PTVs and parotids, a medial displacement in COM, and alterations of the mean dose to the parotid glands as compared to the initial plans. Differences were observed for the dose volume coverage of the PTVs and NTCP of the parotid gland values between the initial plan and our proposed method utilizing deformable registration-based dose calculations.
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Date Issued
2015
PURL
http://purl.flvc.org/fau/fd/FA00004491
Subject Headings
Cancer -- Radiation therapy, Head -- Cancer -- Treatment, Medical physics, Neck -- Cancer -- Treatment, Radiation dosimetry
Format
Document (PDF)
A Computational Study on Different Penalty Approaches for Constrained Optimization in Radiation Therapy Treatment Planning with a Simulated Annealing Algorithm
Title
A Computational Study on Different Penalty Approaches for Constrained Optimization in Radiation Therapy Treatment Planning with a Simulated Annealing Algorithm.
Creator
Mohammadi Khoroushadi, Mohammad Sadegh, Kalantzis, Georgios, Florida Atlantic University, Charles E. Schmidt College of Science, Department of Physics
Abstract/Description
Intensity modulated radiation therapy (IMRT) is a cancer treatment method in which the intensities of the radiation beams are modulated; therefore these beams have non-uniform radiation intensities. The overall result is the delivery of the prescribed dose in the target volume. The dose distribution is conformal to the shape of the target and minimizes the dose to the nearby critical organs. An inverse planning algorithm is used to obtain those non-uniform beam intensities. In inverse...
Show moreIntensity modulated radiation therapy (IMRT) is a cancer treatment method in which the intensities of the radiation beams are modulated; therefore these beams have non-uniform radiation intensities. The overall result is the delivery of the prescribed dose in the target volume. The dose distribution is conformal to the shape of the target and minimizes the dose to the nearby critical organs. An inverse planning algorithm is used to obtain those non-uniform beam intensities. In inverse treatment planning, the treatment plan is achieved by using an optimization process. The optimized plan results to a high-quality dose distribution in the planning target volume (PTV), which receives the prescribed dose while the dose that is received by the organs at risk (OARs) is reduced. Accordingly, an objective function has to be defined for the PTV, while some constraints have to be considered to handle the dose limitations for the OARs.
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Date Issued
2016
PURL
http://purl.flvc.org/fau/fd/FA00004765
Subject Headings
Image-guided radiation therapy., Radiation--Dosage., Mathematical optimization., Evolutionary programming (Computer science), Medical physics., Medical radiology--Data processing.
Format
Document (PDF)